MORStreaming: A Multioutput Regression System for Streaming Data

Yu, Hang; Lu, Jie; Zhang, Guangquan

doi:10.1109/tsmc.2021.3102978

Cited by 12 publications

(10 citation statements)

References 47 publications

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“…In [ 48 ], a comparative study of Apache Spark MLlib and MOA is presented. In [ 49 ], the MORStreaming algorithm predicts two or more values in a row and quickly adapts to changes.…”

Section: Related Workmentioning

confidence: 99%

A new Apache Spark-based framework for big data streaming forecasting in IoT networks

et al. 2023

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“…In [ 48 ], a comparative study of Apache Spark MLlib and MOA is presented. In [ 49 ], the MORStreaming algorithm predicts two or more values in a row and quickly adapts to changes.…”

Section: Related Workmentioning

confidence: 99%

A new Apache Spark-based framework for big data streaming forecasting in IoT networks

et al. 2023

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“…An online multi‐output regression system called MORStreaming (Multi‐Output Regression System for Streaming Data) was proposed in Yu et al (2022) for solving multiple‐output regression problem of streaming data. MORStreaming used an incremental topology learning to select a sub‐set of the historical data (i.e., sampling) to represent the current data distribution.…”

Section: Related Workmentioning

confidence: 99%

“…The theoretical temporal complexity of the ensemble regressor/model is positively related to the number and complexity of the submodels (Kadlec & Gabrys, 2011; Xiao et al, 2019; Yu et al, 2022). Since MORSTS relies on several multiple regressor submodels, the complexity of these collective models remains a challenge (Duarte & Gama, 2015; Elwell & Polikar, 2009; Kolter & Maloof, 2005) because of the non‐uniformity of the complexity of the submodels.…”

Section: Morsts: Multiple Output Regression Algorithmmentioning

confidence: 99%

“…In our work, we adopt a multi‐output regression formalism. Most of the existing multi‐output regression algorithms (Yu et al, 2022) operate on a batch mode and adopt the independent and identically distributed (i.i.d) data assumption. Conversely, streaming regression relies on sequential data ingestion, makes predictions for a limited time and may deal with concept drift.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐output regression for imbalanced data stream

Peng,

Sellami,

Boucelma

et al. 2023

Expert Systems

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In this article, we describe an imbalanced regression method for making predictions over imbalanced data streams. We present MORSTS (Multiple Output Regression for Streaming Time Series), an online ensemble regressors devoted to non‐stationary and imbalanced data streams. MORSTS relies on several multiple output regressor submodels, adopts a cost sensitive weighting technique for dealing with imbalanced datasets, and handles overfitting by means of the K‐fold cross validation. For assessment purposes, experiments have been conducted on known real datasets and compared with known base regression techniques.

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“…The method introduces a new TSK fuzzy rule induction strategy by combining the merits of the rule induction concept implemented in AMRules with the expressive power of TSK fuzzy rules, which solves the problem of adaptive learning from evolving data streams. Yu et al [ 23 ] proposed an online multi-output regression algorithm called MORStreaming, which learns instances based on topological networks and correlations between outputs based on adaptive rules and can solve the problem of multiple output regression in the data stream environment.…”

Section: Introductionmentioning

confidence: 99%

Online-Dynamic-Clustering-Based Soft Sensor for Industrial Semi-Supervised Data Streams

Wang

Jin

Chen

et al. 2023

Sensors

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In the era of big data, industrial process data are often generated rapidly in the form of streams. Thus, how to process such sequential and high-speed stream data in real time and provide critical quality variable predictions has become a critical issue for facilitating efficient process control and monitoring in the process industry. Traditionally, soft sensor models are usually built through offline batch learning, which remain unchanged during the online implementation phase. Once the process state changes, soft sensors built from historical data cannot provide accurate predictions. In practice, industrial process data streams often exhibit characteristics such as nonlinearity, time-varying behavior, and label scarcity, which pose great challenges for building high-performance soft sensor models. To address this issue, an online-dynamic-clustering-based soft sensor (ODCSS) is proposed for industrial semi-supervised data streams. The method achieves automatic generation and update of clusters and samples deletion through online dynamic clustering, thus enabling online dynamic identification of process states. Meanwhile, selective ensemble learning and just-in-time learning (JITL) are employed through an adaptive switching prediction strategy, which enables dealing with gradual and abrupt changes in process characteristics and thus alleviates model performance degradation caused by concept drift. In addition, semi-supervised learning is introduced to exploit the information of unlabeled samples and obtain high-confidence pseudo-labeled samples to expand the labeled training set. The proposed method can effectively deal with nonlinearity, time-variability, and label scarcity issues in the process data stream environment and thus enable reliable target variable predictions. The application results from two case studies show that the proposed ODCSS soft sensor approach is superior to conventional soft sensors in a semi-supervised data stream environment.

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MORStreaming: A Multioutput Regression System for Streaming Data

Cited by 12 publications

References 47 publications

A new Apache Spark-based framework for big data streaming forecasting in IoT networks

A new Apache Spark-based framework for big data streaming forecasting in IoT networks

Multi‐output regression for imbalanced data stream

Online-Dynamic-Clustering-Based Soft Sensor for Industrial Semi-Supervised Data Streams

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